AI Agents Are Changing How We Buy Software, and Proven Is Ready

If you ask platform teams and operators, they'll tell you the software vendor selection process doesn’t feel broken at first. It just feels… heavier than it should be.

You start with a clear need. Then comes the familiar sequence: defining evaluation criteria, shortlisting potential vendors, running demos, comparing pricing models, and aligning key stakeholders. Somewhere along the way, the process expands. More tools enter the mix. More opinions. More edge cases. What began as a simple decision turns into a layered exercise in vendor evaluation.

And even after all that effort, there’s still uncertainty.

? Are we choosing the right software provider?

? Are we missing hidden costs?

? Will this integrate seamlessly with our existing systems?

? What does vendor performance actually look like six months from now?

These questions sit at the core of modern procurement strategy. And increasingly, they’re becoming harder to answer with the tools and processes most teams rely on today.

The Growing Weight of Vendor Selection

Across startups and large organizations alike, the volume of SaaS vendors has exploded. What used to be a manageable set of options is now a crowded landscape of specialized tools, each promising better operational efficiency or improved business performance.

At the same time, expectations have risen.

Internal teams expect tools to deliver immediate value. End users want a seamless user experience. And regulators expect strict adherence to industry standards and regulatory compliance. For procurement teams and operators alike, this creates tension. There's a need to move fast without compromising risk management. 

In practice, this means the vendor selection process now involves:

• Assessing integration capabilities across increasingly complex stacks
• Reviewing compliance certifications to avoid regulatory violations
• Evaluating financial stability and long-term vendor’s reliability
• Comparing service level agreements and real-world service delivery
• Understanding the full total cost, including long-term implications

Even with structured vendor management, most teams are still relying on fragmented inputs. Reviews, references, sales conversations, and internal assumptions all play a role. But rarely do they add up to a clear, data-backed picture.

That gap is where mistakes occur, leading to poor supplier selection, underperforming tools, and avoidable supply chain disruptions.

Where Things Start to Break

A tool looks strong during the vendor selection process. It meets the stated vendor selection criteria, performs well in demos, and aligns with immediate needs. But once implemented, the reality is different.

Integration takes longer than expected. What seemed like strong integration capabilities requires a significant data migration effort. The product introduces friction instead of improving operational efficiency. Support is slower than expected. And the promised value takes longer to materialize.

At that point, the real cost becomes visible. Not just the subscription, but:

• Time lost across engineering and operations
• Delays in key initiatives
• Additional tools introduced to compensate

The issue is that the inputs available during vendor evaluation didn't fully reflect real-world outcomes.

Why the Current Model Struggles

Traditional vendor selection is built around human interpretation. It involves gathering information from various sources and applying judgment across different criteria. This includes evaluating technical specifications, examining a product roadmap, and estimating long-term compatibility based on limited insight into past performance.

Even experienced procurement professionals find it difficult to do all this across vendors in a way that feels rigorous.

Consider what goes into evaluating vendors today:

You’re trying to understand past results using limited past performance metrics. You’re assessing future potential based on a product roadmap that may or may not materialize. You’re estimating implementation effort, including data migration, without full visibility into complexity. And you’re often negotiating terms like payment schedules, pricing, and support without a clear benchmark for what “good” looks like.

All of this introduces friction into decision-making. It also shifts focus away from higher-value strategic tasks toward coordination, comparison, and reconciliation of information. The process works, but it doesn’t scale well with the growing complexity of modern software ecosystems.

Enter AI Agents

We're seeing AI agents begin to change the problem's structure. Instead of relying on humans to manually gather and interpret information, agents can process large volumes of structured and unstructured data in real time. They don’t replace judgment entirely, but they reshape how information is surfaced and evaluated.

In a practical sense, this means an agent can:

• Analyze historical performance data across comparable companies
• Evaluate supplier performance using consistent benchmarks
• Compare pricing models and identify true cost savings opportunities
• Assess integration capabilities against your existing systems or ERP system
• Flag compliance risks and gaps in regulatory compliance
• Weigh technical specifications against your actual requirements

What changes is not just speed, but consistency. The same evaluation framework can be applied across every software vendor, reducing bias and improving clarity.

This has a direct impact on risk mitigation. Instead of relying on partial information, teams can make decisions based on a more complete view of past performance, supplier risk, and long-term fit.

What Actually Changes in Vendor Selection?

 The usual protocol of vendor selection and choosing a strong fit depending on your needs still holds. What changes is the quality of the inputs behind those decisions.

In a typical process today, a vendor can appear strong because they present well. The demo is smooth. The narrative is clear. References check out. On paper, they meet the vendor selection criteria. But much of that signal is curated. It reflects how the vendor wants to be seen, not necessarily how they perform over time.

With better data and the support of AI agents, that gap starts to close. Instead of relying on isolated signals, teams can see how a vendor actually behaves across comparable environments. Not just whether they can deliver, but whether they consistently do. Patterns start to emerge around on-time delivery, the reliability of their service delivery, and how their product holds up once it’s embedded in real workflows.

This also changes how teams think about fit.

 A tool that looks strong in isolation may introduce friction when it meets your existing systems. Integration stops being a theoretical checkbox and becomes something you can evaluate based on real outcomes. You begin to understand where vendors typically struggle, how much effort is required to make them work, and whether that effort is justified by the value they deliver.

Pricing becomes clearer as well.

Rather than focusing solely on headline numbers, teams can see how pricing models evolve over time, where hidden costs tend to appear, how usage scales, and what the true total cost looks like in practice. That makes it easier to judge whether a vendor is offering fair pricing.

All of this leads to a different kind of confidence. Decisions are no longer based on how convincing a vendor is in a controlled setting. They’re based on how that vendor performs in environments that look like yours.

Over time, that reduces outcome variability and leads to fewer surprises after implementation. You also get fewer cases where a tool needs to be replaced or heavily adapted. And that’s where the real impact shows up because you have more predictable business performance.

Once you start looking at vendor decisions through this lens, something else becomes obvious. Human-driven sales processes are no longer the most efficient way to choose a vendor. The traditional controlled environment, which starts with a demo and ends with a sales presentation, is becoming obsolete.

Now we're entering the era where AI agents are being tasked with recommending solutions, which introduces a new set of challenges that require more structure.

Our founder shares his perspective below on the impact of AI Agents in 2026.

So, What Is Agent Ready Software?

It’s software that can be understood, evaluated, and selected by machines without requiring interpretation from a human buyer. That requires a different level of clarity.

Agent-ready software exposes:

1. Structured, machine-readable data
2. Transparent pricing and fair pricing logic
3. Clear documentation of integration capabilities
4. Verified signals of service delivery and outcomes
5. Evidence of compliance with relevant industry standards

It also reduces ambiguity. Instead of relying on narrative, it provides concrete signals to support faster, more accurate decision-making. For operators and platform teams, this means less time spent screening vendors and more confidence in the final choice.

Where Proven Fits In

In 2026, this is the new direction Proven is building toward. 

For over a decade, Proven has been partnering with VC and PE platform teams as well as banks and government agency to streamline their vendor management. Today, we've evolved with advancing technologies to help organizations identify trusted software vendors through a curated network, which reduces friction in vendor selection by surfacing relevant, personalized options and facilitating access to high-quality providers. But the longer-term shift is even more structural and nuanced.

Proven is evolving into a layer where:

1. Vendor data is standardized and comparable
2. Evaluation criteria are consistent across the platform
3. Signals like supplier performance, vendor’s ability, and real-world usage are clearly visible
4. Advanced analytics and predictive analytics support better decision-making

This changes how teams approach procurement strategy. Instead of reacting to immediate needs, they can take a more proactive approach, identifying the right vendors earlier and building stronger collaborative partnerships.

Over time, this also supports better performance management. Decisions are no longer one-off events but part of an ongoing system of evaluation and improvement.

What This Means in Practice

For platform teams and operators, this shift shows up in very concrete ways and it usually starts with a familiar situation.

A portfolio company needs a new tool. Maybe it’s compliance, finance, or data infrastructure. The team moves quickly. They shortlist a few SaaS vendors, run a lightweight vendor evaluation, and make a decision based on a mix of demos, references, and perceived fit.

Three months later, the cracks start to show. The product doesn’t integrate well with their existing systems. What looked like strong integration capabilities in a sales call turns into weeks of unexpected data migration work. The user experience slows adoption internally. Support is slower than expected, despite what was promised in the service level agreements. And suddenly, what was meant to improve operational efficiency is creating drag.

At that point, the real cost becomes subscription dollar amount plus:

• Time lost across engineering and ops teams
• Delays in critical initiatives tied to that tool
• Additional vendors brought in to patch gaps
• Increased supplier risk as workarounds pile up

This is when most teams start to realize the gap between vendor selection criteria and actual outcomes.

Why We Started Building Differently

This is what pushed us to rethink how vendor decisions should be supported.

Instead of focusing only on discovery, we started working toward surfacing better signals grounded in real usage, measurable outcomes, and consistent performance management.

That includes:

• Deeper visibility into historical performance data
• Clearer understanding of vendor’s ability to deliver over time
• Structured insight into pricing models and long-term cost
• Better indicators of compliance certifications and risk exposure

At the same time, advances in AI tools and advanced analytics made it possible to apply these signals at scale.

What once required extensive manual comparison can now be supported through systems that continuously analyze and update performance insights.

The Beginning of a New Process to Evaluate Vendors

The way organizations approach vendor selection is changing at a structural level.

AI agents are accelerating decision-making while transforming the decision-making process, emphasizing data, performance, and tailored solutions. For operators and platform teams, this leads to improved alignment and more dependable results.

Platforms like Proven now have the chance to bridge a long-standing gap between vendor evaluations and actual performance. This gap is a major source of costs, risks, and inefficiencies, and it is now finally feasible to tackle it effectively.

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